LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models

Yadong Zhang, Shaoguang Mao, Tao Ge, Xun Wang, Yan Xia, Wenshan Wu, Ting Song, Man Lan, Furu Wei

Research Area: Evaluation, Inference algorithms for LMs, Human mind, brain, philosophy, laws and LMs, LMs on diverse modalities and novel applications

Keywords: Large Language Model,Strategic Reasoning,Multi-Agent System,Decision-Making,Advanced Reasoning

TL;DR: A comprehensive overview of the state of LLMs in strategic reasoning, shedding light on their capabilities, applications, and the road ahead for harnessing their potential more effectively.

Abstract: This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that necessitates understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly. Strategic reasoning is distinguished by its focus on the dynamic and uncertain nature of interactions among multi-agents, where comprehending the environment and anticipating the behavior of others is crucial. We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with LLMs, highlighting the burgeoning development in this area and the interdisciplinary approaches enhancing their decision-making performance. It aims to systematize and clarify the scattered literature on this subject, providing a systematic review that underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.

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Submission Number: 668